In the oil and gas industry, reservoir monitoring, sometimes referred to as reservoir surveillance, involves the regular collection and monitoring of measured production data from within and around the wells of a reservoir. Such data may include, but is not limited to, water saturation, fluid pressure, fluid flow rates, fluid temperature, and the like. As the data is collected, it is archived into a historical database.
The archived data, however, mostly reflects conditions immediately around the reservoir wells. Simulations can model the overall behavior of the entire network of wells and surface facilities based on the data, both current and historical, to provide a more complete picture. These simulations produce simulated interwell data both near and at a distance from the wells. Simulated near-well data is correlated against measured near-well data, and the modeling parameters are adjusted as needed to reduce the error between the simulated and measured data. Once so adjusted, the simulated interwell data, both near and at a distance from the well, may be relied upon to assess the overall state of the network of wells and surface facilities. Such data may also be relied upon to predict future behavior based upon either actual or hypothetical conditions input by an operator of the simulator.
However, such simulations, particularly those that perform full-physics numerical simulations of large reservoirs, are computationally intensive and can take hours, even days, to execute. Additionally, the simulations use equations that are highly non-linear, and often require damping in order for the iterations to converge. As a result, nonlinearities in one part of the network can cause slow convergence of the solution for the entire network. Finally, solving the network equations in parallel results in an overwhelming amount of dependencies and messages. Specifically, some processes in the parallel solving algorithm are dependent upon the results of other processes and must wait for the other processes to resolve and send a message with the results. As the number of wells in a network increase, in some cases to over 1,000 wells, these dependencies and messages constitute a majority of the computation time.
It should be understood, however, that the specific embodiments given in the drawings and detailed description thereto do not limit the disclosure. On the contrary, they provide the foundation for one of ordinary skill to discern the alternative forms, equivalents, and modifications that are encompassed together with one or more of the given embodiments in the scope of the appended claims.